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Assessment of forest restoration with multitemporal remote sensing imagery

Climate variability and man-made impacts have severely damaged forests around the world in recent years, which calls for an urgent need of restoration aiming toward long-term sustainability for the forest environment. This paper proposes a new three-level decision tree (TLDT) approach to map forest,...

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Autores principales: Liu, Cheng-Chien, Chen, Yi-Hsin, Wu, Mei-Heng Margaret, Wei, Chiang, Ko, Ming-Hsun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6513895/
https://www.ncbi.nlm.nih.gov/pubmed/31086217
http://dx.doi.org/10.1038/s41598-019-43544-5
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author Liu, Cheng-Chien
Chen, Yi-Hsin
Wu, Mei-Heng Margaret
Wei, Chiang
Ko, Ming-Hsun
author_facet Liu, Cheng-Chien
Chen, Yi-Hsin
Wu, Mei-Heng Margaret
Wei, Chiang
Ko, Ming-Hsun
author_sort Liu, Cheng-Chien
collection PubMed
description Climate variability and man-made impacts have severely damaged forests around the world in recent years, which calls for an urgent need of restoration aiming toward long-term sustainability for the forest environment. This paper proposes a new three-level decision tree (TLDT) approach to map forest, shadowy, bare and low-vegetated lands sequentially by integrating three spectral indices. TLDT requires neither image normalization nor atmospheric correction, and improves on the other methods by introducing more levels of decision tree classification with inputs from the same multispectral imagery. This approach is validated by comparing the results obtained from aerial orthophotos (25 cm) that were acquired at approximately the same time in which the Formosa-2 images (8 m) were being taken. The overall accuracy is as high as 96.8% after excluding the deviations near the boundary of each class caused by the different resolutions. With TLDT, the effectiveness of forest restoration at 30 sites are assessed using all available multispectral Formosat-2 images acquired between 2005 and 2016. The distinction between natural regeneration and regrowth enhanced by restoration efforts were also made by using the existing dataset and TLDT developed in this research. This work supports the use of multitemporal remote sensing imagery as a reliable source of data for assessing the effectiveness of forest restoration on a regular basis. This work also serves as the basis for studying the global trend of forest restoration in the future.
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spelling pubmed-65138952019-05-24 Assessment of forest restoration with multitemporal remote sensing imagery Liu, Cheng-Chien Chen, Yi-Hsin Wu, Mei-Heng Margaret Wei, Chiang Ko, Ming-Hsun Sci Rep Article Climate variability and man-made impacts have severely damaged forests around the world in recent years, which calls for an urgent need of restoration aiming toward long-term sustainability for the forest environment. This paper proposes a new three-level decision tree (TLDT) approach to map forest, shadowy, bare and low-vegetated lands sequentially by integrating three spectral indices. TLDT requires neither image normalization nor atmospheric correction, and improves on the other methods by introducing more levels of decision tree classification with inputs from the same multispectral imagery. This approach is validated by comparing the results obtained from aerial orthophotos (25 cm) that were acquired at approximately the same time in which the Formosa-2 images (8 m) were being taken. The overall accuracy is as high as 96.8% after excluding the deviations near the boundary of each class caused by the different resolutions. With TLDT, the effectiveness of forest restoration at 30 sites are assessed using all available multispectral Formosat-2 images acquired between 2005 and 2016. The distinction between natural regeneration and regrowth enhanced by restoration efforts were also made by using the existing dataset and TLDT developed in this research. This work supports the use of multitemporal remote sensing imagery as a reliable source of data for assessing the effectiveness of forest restoration on a regular basis. This work also serves as the basis for studying the global trend of forest restoration in the future. Nature Publishing Group UK 2019-05-13 /pmc/articles/PMC6513895/ /pubmed/31086217 http://dx.doi.org/10.1038/s41598-019-43544-5 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Liu, Cheng-Chien
Chen, Yi-Hsin
Wu, Mei-Heng Margaret
Wei, Chiang
Ko, Ming-Hsun
Assessment of forest restoration with multitemporal remote sensing imagery
title Assessment of forest restoration with multitemporal remote sensing imagery
title_full Assessment of forest restoration with multitemporal remote sensing imagery
title_fullStr Assessment of forest restoration with multitemporal remote sensing imagery
title_full_unstemmed Assessment of forest restoration with multitemporal remote sensing imagery
title_short Assessment of forest restoration with multitemporal remote sensing imagery
title_sort assessment of forest restoration with multitemporal remote sensing imagery
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6513895/
https://www.ncbi.nlm.nih.gov/pubmed/31086217
http://dx.doi.org/10.1038/s41598-019-43544-5
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